A weighted estimating equation for linear regression with missing covariate data.
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Pattern-mixture models for analyzing normal outcome data with proxy respondentsPerformance of methods for handling missing categorical covariate data in population pharmacokinetic analyses.Evidence for henipavirus spillover into human populations in Africa.On protected estimation of an odds ratio model with missing binary exposure and confoundersSemiparametric regression models for repeated measures of mortal cohorts with non-monotone missing outcomes and time-dependent covariatesWeighted estimating equations for longitudinal studies with death and non-monotone missing time-dependent covariates and outcomes.Age and sex of surgeons and mortality of older surgical patients: observational study.
P2860
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P2860
A weighted estimating equation for linear regression with missing covariate data.
description
2002 nî lūn-bûn
@nan
2002 թուականի Օգոստոսին հրատարակուած գիտական յօդուած
@hyw
2002 թվականի օգոստոսին հրատարակված գիտական հոդված
@hy
2002年の論文
@ja
2002年論文
@yue
2002年論文
@zh-hant
2002年論文
@zh-hk
2002年論文
@zh-mo
2002年論文
@zh-tw
2002年论文
@wuu
name
A weighted estimating equation for linear regression with missing covariate data.
@ast
A weighted estimating equation for linear regression with missing covariate data.
@en
type
label
A weighted estimating equation for linear regression with missing covariate data.
@ast
A weighted estimating equation for linear regression with missing covariate data.
@en
prefLabel
A weighted estimating equation for linear regression with missing covariate data.
@ast
A weighted estimating equation for linear regression with missing covariate data.
@en
P2093
P2860
P356
P1476
A weighted estimating equation for linear regression with missing covariate data.
@en
P2093
Michael Parzen
Steven Lipshultz
Stuart R Lipsitz
P2860
P304
P356
10.1002/SIM.1195
P407
P577
2002-08-01T00:00:00Z